Ensemble learning
What Is Ensemble? The ensemble methods in machine learning combine the insights obtained from multiple learning models to facilitate accurate and improved decisions. These methods follow the same principle as the example of buying an air-conditioner cited above. In learning models, noise, variance, and bias are the major sources of error. The ensemble methods in machine learning help minimize these error-causing factors, thereby ensuring the accuracy and stability of machine learning (ML) algorithms . Example 1: Assume that you are developing an app for the travel industry. It is obvious that before making the app public, you will want to get crucial feedback on bugs and potential loopholes that are affecting the user experience. What are your available options for obtaining critical feedback? 1) Soliciting opinions from your parents, spouse, or close friends. 2) Asking your co-workers who travel regularly and then evaluating their response. 3) Rolling out your travel and tourism app